As the world has become more reliant on computers and information exchange, the need for reliable data transmission has become increasingly important. One key element in the exchange of information is the accurate and efficient transmission and reception of data across noisy transmission channels.
Signal processing methods implemented in practical communications systems are usually designed under the assumption that any underlying noise and interference is Gaussian. Although this assumption finds strong theoretical justification in the central limit theorem, the noise and interference patterns commonly present in modern mobile communications systems are far from Gaussian. Noise and interference generally exhibit “impulsive” behavior. In typical mobile communication systems, noise and interference sources often include: motor-vehicle ignition noise, switching noise from electromechanical equipment, thunderstorms, and heavy bursts of interference. Current signal processing systems are not designed to handle these non-Gaussian noise sources. Accordingly, these systems may perform poorly, and might even fail, in the presence of impulsive noise.
Channel noise and interference can be effectively modeled as the superposition of many small and independent effects. In practice, these effects do not always follow a Gaussian distribution. This situation appears to contradict the central limit theorem. For many years, engineers have been unable to explain this apparent contradiction. Consequently, many of the techniques developed to cope with impulsive noise were mainly ad hoc, largely based on signal clipping and filtering prior to application of a Gaussian-based technique.
Clipping the amplitude of an input signal is only effective if the amplitude of the input signal is above or below specific threshold values. These threshold values are typically determined by the limits of hardware used in a receiver in a communication system. Accordingly, the threshold values are often chosen in order to take advantage of the full dynamic range of analog to digital (A/D) converter(s) used in such a receiver. However, if impulsive noise added to the input signal does not cause the amplitude of a signal to exceed a specific threshold, clipping will not remove the noise. Additionally, even when noise does cause the signal to exceed a threshold, clipping only removes noise to the extent that the magnitude of the signal plus the noise is above the threshold. Accordingly, noise is not actually removed, though its effects are somewhat reduced.
When individual signals within a sequence are contaminated by noise, the sequence may not be properly decoded, thereby making communications difficult. In typical communication systems, decoding is used to identify potential communication errors. Additionally, decoding may be able to correct some, or even most, errors. Errors may be corrected by one of many error detection and correct schemes known to those skilled in the art. Typical coding and decoding schemes are able to correct errors by inserting controlled redundancy into a transmitted information stream. This is typically performed by adding additional bits or using an expanded channel signal set. These schemes allow a receiver to detect, and possibly correct, errors.
In its most simple form, one problem with noisy transmission environments is that, a certain percentage of the time, a transmitted ‘1’ is received as a ‘0’ or vice versa. There are many methods of encoding data that allow received errors to be detected or even corrected. These encoding and decoding schemes are typically optimized based on a set of underlying assumptions. Preferably, these assumptions are designed to match the conditions of a real-world communications environment. Often, systems using these schemes are designed under the assumption that the underlying noise and interference is Gaussian. When these assumptions do not match real-world conditions, the performance of these schemes may no longer be optimal. While systems which use these schemes work well a majority of the time, their performance is severely affected when conditions degrade.
One way to accommodate increased noise in a transmission channel is to build a high level of redundancy into the encoding scheme. The problem with such solutions is that adding redundancy increases the size of each transmission frame. Those skilled in the art are familiar with the tradeoffs between using highly redundant encoding schemes, which allow the detection and correction of a greater number of reception errors, and using a scheme with lower redundancy, which has a smaller frame, and thus allows a greater quantity of data to be transmitted in a given time period at the expense of being able to detect and correct few reception errors. While these solutions may be somewhat effective, the tradeoff between accuracy and speed limits optimal performance.
Another solution for reducing the effects of noise on a transmission channel is to use multiple transmission channels for each transmission. Such schemes, called diversity schemes, transmit the same data frame on multiple channels. When the data is received, each channel is checked for accuracy and a logical decision engine, or a combiner, chooses a received signal from one of the channels that is believed to be accurate. An example of a receiver system using a diversity scheme is shown in FIG. 1.
The classical goal of a system based on a diversity scheme is to provide the receiver with L versions of an information signal transmitted over independent channels. The parameter L is the diversity order of the system. There are many ways to introduce diversity into a system. Well-known examples include frequency, time and space diversity. The RAKE receiver is a diversity technique commonly employed to combat error bursts or “deep fades” over a multipath fading channel. The basic idea is that the provisioning of multiple, independent versions of a transmitted signal greatly reduces the impact of fading. One weak signal can be compensated by other strong signals. Hence, diversity addresses the issue of robust error performance in a fading environment.
There are several well-known methods used to combine the L diversity versions of a signal that reach a receiver. The most fundamental combining techniques include selection combining, equal-gain combining, and maximal-ratio combining.
These schemes may be successful in reducing the effects of noise because it is unlikely that all of the channels will be simultaneously corrupted by noise. However, the overhead (i.e., cost of additional hardware) associated with such a scheme is large because the system utilizes multiple transmitters, receivers, and broadcast channels. The use of multiple broadcast channels is also undesirable because it requires significantly more bandwidth than normal broadcast schemes.
Therefore, there is a need in the art for systems and methods for accurately and efficiently encoding and decoding transmission signals in varying transmission conditions.